IAPS AI Reliability Survey

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Institute for AI Policy and Strategy·iaps.ai/research/ai-reliability-survey

Credibility Rating

4/5

High(4)

High quality. Established institution or organization with editorial oversight and accountability.

Rating inherited from publication venue: Institute for AI Policy and Strategy

Useful for researchers and policymakers seeking an expert-consensus snapshot of priority areas in AI reliability and security; produced by IAPS, which focuses on the intersection of AI safety and policy.

Metadata

Importance: 62/100organizational reportanalysis

Summary

A structured expert survey conducted by the Institute for AI Policy and Strategy (IAPS) that maps and prioritizes research directions in AI reliability and security. The study aggregates expert opinion to produce data-driven rankings of research areas by urgency and potential impact. It serves as a resource for researchers and policymakers seeking to allocate effort across the AI safety and reliability landscape.

Key Points

•Aggregates expert perspectives to rank AI reliability and security research directions by urgency and expected impact.
•Covers a broad landscape including technical safety, robustness, and security considerations for deployed AI systems.
•Provides data-driven recommendations useful for prioritizing research agendas and funding decisions.
•Produced by IAPS, a policy-focused organization bridging technical AI safety and governance communities.
•Serves as a reference point for identifying consensus and disagreement among experts on key research gaps.

Review

The IAPS AI Reliability Survey represents a significant effort to systematically assess expert perspectives on AI safety research priorities. By surveying 53 specialists across 105 technical research areas, the study provides a nuanced mapping of where investment and attention are most critically needed in ensuring AI system reliability and security. The research highlights several key insights, including the urgent need for robust early warning systems, multi-agent system research, and comprehensive capability evaluations. Notably, the survey reveals a broad consensus among experts about actionable research opportunities, with 52 out of 53 experts identifying at least one research direction as both important and tractable. The study's policy recommendations span direct funding, investment incentivization, research coordination, talent pipeline development, and expanding researcher access to critical AI models.

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Expert Survey: AI Reliability & Security Research Priorities &mdash; Institute for AI Policy and Strategy 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 

 
 
 

 
 
 

 
 
 
 
 
 
 

 
 
 
 
 
 

 
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 Expert Survey: AI Reliability & Security Research Priorities

 
 
 

 
 
 May 23 
 
 Written By Joe O'Brien 
 
 
 
 

 
 
 

 
 
 Read the full report
 
 

 

 

 
 
 Our survey of 53 specialists across 105 AI reliability and security research areas identifies the most promising research prospects to guide strategic AI R&D investment. As companies are seeking to develop AI systems with broadly human-level capabilities, research on reliability and security is urgently needed to ensure AI's benefits can be safely and broadly realized and prevent severe harms. To inform strategic investment, we asked 53 experts to rate subsets from a list of 105 technical AI reliability and security research areas on importance and tractability.

 This study is the first to quantify expert priorities across a comprehensive taxonomy of AI safety and security research directions and to produce a data-driven ranking of their potential impact. These rankings may support evidence-based decisions about how to effectively deploy resources toward AI reliability and security research.

 Our survey revealed the following:

 Highly promising research directions centered around robust early warning and monitoring of AI risks. Some of the most promising sub-areas included specific capability evaluations (e.g., CBRN, cyber, and deception), understanding emergence and scaling laws, and advancing agent oversight.

 Multi-agent systems emerged as a critical priority. All multi-agent research areas ranked in the top 30, suggesting these systems present novel risks distinct from single agents that require urgent attention.

 Experts strongly valued improving both specific AI evaluations, and the science of evals. 6 of the top 10 approaches focus on evaluating dangerous capabilities.

 We identified high-importance but challenging areas requiring more substantive investments of time and research. Access control and interface hardening, supply chain integrity, weight security, and confidential computing all rated highly on importance but low on tractability

 For immediate impact (<$10M, 2 years): Fund dangerous-capability evaluations, scalable oversight tools, and multi-agent metrics, oversight, and monitoring—areas with strong expert consensus but currently undercapitalized relative to risk.

 Notably, 52 of 53 experts identified at least one research direction as both important and tractable , demonstrating broad optimism about accessible, actionable opportunities in AI reliability and security research.

 We also provide several policy recommendations:

 Direct funding: Congress and relevant executive agencies, such as the National Science Foundation, should c

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